opioid_data_wide = read_csv(file="Wide_Master.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## .default = col_double(),
## State = col_character(),
## County = col_character(),
## `GDP Education, Health, Social Assistance` = col_character()
## )
## i Use `spec()` for the full column specifications.
counties = read_sf(dsn ="C:\\Users\\zgord\\OneDrive - University of North Carolina at Chapel Hill\\Semesters\\Spring 2021\\STOR 320\\Project\\data\\cb_2018_us_county_5m\\cb_2018_us_county_5m.shp")
fips = read.csv("us-state-ansi-fips.csv")
# clean data. Done as a group
opioid_data_wide1 <- opioid_data_wide %>%
filter(Year == '2016') %>%
filter(!is.na(Total))
opioid_data_wide1 <- opioid_data_wide1 %>%
mutate(HS_Or_Less = (HS_Grad + Less_Than_HS)/(HS_Grad + Less_Than_HS + Bachelor_Degree + Grad_Degree + Associates_Degree)) %>%
mutate(HS_Or_Less_Quantile = ntile(HS_Or_Less, 4)) %>%
mutate(Some_College = (Bachelor_Degree + Grad_Degree + Associates_Degree)/(HS_Grad + Less_Than_HS + Bachelor_Degree + Grad_Degree + Associates_Degree)) %>%
mutate(Some_College_Quantile = ntile(Some_College, 4)) %>%
mutate(Proportion_Non_US = Non_US_Born/Population) %>%
mutate(Non_US_Quantile = ntile(Proportion_Non_US, 20)) %>%
mutate(PopDensity = Population/LandArea, Deaths_Per_100000 = (Total/Population) * 100000) %>%
mutate(GDP_Per_Capita = `GDP Total`/Population) %>%
mutate(GDP_EHSA_Per_Capita = as.numeric(`GDP Education, Health, Social Assistance`)/Population) %>%
mutate(GDP_PerCap_Quantile = ntile(GDP_Per_Capita, 4)) %>%
mutate(GDP_EHSA_PerCap_Quantile = ntile(GDP_EHSA_Per_Capita, 4)) %>%
mutate(Social_Transportation = Bike + Walk + Carpool + Public) %>%
mutate(AntiSocial_Transportation = Alone + Home)
## Warning: Problem with `mutate()` input `GDP_EHSA_Per_Capita`.
## i NAs introduced by coercion
## i Input `GDP_EHSA_Per_Capita` is `as.numeric(`GDP Education, Health, Social Assistance`)/Population`.
opioid_data_wide1
## # A tibble: 583 x 39
## State County Year Total Heroin Other Methadone Population LandArea Bike
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Alab~ Baldw~ 2016 16 NA 12 NA 208563 1590. 0.1
## 2 Alab~ Cullm~ 2016 12 NA NA NA 82471 735. NA
## 3 Alab~ Jeffe~ 2016 116 80 31 14 659521 1111. 0.1
## 4 Alab~ Mobile 2016 18 NA 11 NA 414836 1229. 0.1
## 5 Alab~ Shelby 2016 17 10 NA NA 210622 785. 0
## 6 Alab~ St. C~ 2016 10 NA NA NA 88019 632. NA
## 7 Alas~ Ancho~ 2016 43 30 20 NA NA NA NA
## 8 Alas~ Fairb~ 2016 10 NA NA NA 100605 7338. 0.7
## 9 Alas~ Kenai~ 2016 11 NA NA NA 58506 16075. NA
## 10 Alas~ Matan~ 2016 18 NA 13 NA NA NA 0.1
## # ... with 573 more rows, and 29 more variables: Carpool <dbl>, Alone <dbl>,
## # Public <dbl>, Walk <dbl>, Home <dbl>, Income <dbl>, Non_US_Born <dbl>,
## # Bachelor_Degree <dbl>, Grad_Degree <dbl>, HS_Grad <dbl>,
## # Less_Than_HS <dbl>, Associates_Degree <dbl>, Unemployment <dbl>, `GDP
## # Total` <dbl>, `GDP Education, Health, Social Assistance` <chr>,
## # HS_Or_Less <dbl>, HS_Or_Less_Quantile <int>, Some_College <dbl>,
## # Some_College_Quantile <int>, Proportion_Non_US <dbl>,
## # Non_US_Quantile <int>, PopDensity <dbl>, Deaths_Per_100000 <dbl>,
## # GDP_Per_Capita <dbl>, GDP_EHSA_Per_Capita <dbl>, GDP_PerCap_Quantile <int>,
## # GDP_EHSA_PerCap_Quantile <int>, Social_Transportation <dbl>,
## # AntiSocial_Transportation <dbl>
counties = counties %>%
mutate(STATEFP = as.numeric(STATEFP))
counties.sf.data = opioid_data_wide1 %>%
filter(Year == 2016) %>%
left_join(fips, by = c("State" = "stname")) %>%
right_join(counties, by = c("st" = "STATEFP", "County" = "NAME")) %>%
mutate(county_name = paste(County,", ", State, sep ="")) %>%
st_as_sf()
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(counties.sf.data) +
tm_polygons("Deaths_Per_100000",
id = "county_name",
title = "Opioid eaths per capita in 2016",
popup.vars = c("Deaths Per 100,000: " = "Deaths_Per_100000",
"Population of county: " = "Population"),
)